Bibliographic record
Abstract
Introduction: complete partial orders Having looked at the abstract definition of ‘model’ in the last two chapters, let us now study one particular model in detail. It will be a variant of Dana Scott's D ∞ , which was the first non-trivial model invented, and has been a dominant influence on the semantics of λ-calculus and programming languages ever since. Actually, D ∞ came as quite a surprise to all workers in λ − even to Scott. In autumn 1969 he wrote a paper which argued vigorously that an interpretation of all untyped λ-terms in set theory was highly unlikely, and that those who were interested in making models of λ should limit themselves to the typed version. (For that paper, see [Sco93].) The paper included a sketch of a new interpretation of typed terms. Then, only a month later, Scott realized that, by altering this new interpretation only slightly, he could make it into a model of untyped λ; this was D ∞ . D ∞ is a model of both CL w and λβ, and is also extensional. The description below will owe much to accounts by Dana Scott and Gordon Plotkin, and to the well-presented account in [Bar84], but it will give more details than these and will assume the reader has a less mathematical background. The construction of D ∞ involves notions from topology. These will be defined below. They are very different from the syntactical techniques used in this book so far, but they are standard tools in the semantics of programming languages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".